Generative Artificial Intelligence in Human Resource Management: Opportunities, Risks, and a Hybrid Decision-Making Framework

Abstract

Generative Artificial Intelligence (GAI), particularly large language models (LLMs), is reshaping organizational decision-making across domains, including Human Resource Management (HRM). From recruitment and selection to performance management, learning and development, and employee engagement, AI-powered tools promise efficiency, scalability, and data-driven insight. However, concerns about algorithmic bias, hallucinated outputs, reliability, and ethical governance remain critical. Emerging academic and practitioner discourse reveals both transformative potential and substantial risk in deploying generative AI for strategic HR functions. Community experiences also highlight reliability challenges, including fabricated citations and inaccurate outputs in research contexts. This paper develops a comprehensive conceptual framework for responsible AI integration in HRM. Drawing from contemporary scholarship and practitioner observations, the study proposes a Hybrid Human–AI Decision Architecture (HHADA) model that balances augmentation with human oversight. The paper contributes theoretically by integrating socio-technical systems theory, ethical AI governance, and strategic HRM perspectives. In practice, it offers policy recommendations for organizations seeking to deploy generative AI responsibly in workforce management.